Analytics Life Cycle

Discovery

- Learning the business domain process

- Resources

- Framing the problem

Discovery

- Identifying key stakeholders

- Interviewing analytics sponsor

- Developing initial hypothesis

- Identifiying potential data source

Data Preparation

- Preparing analytics sandbox

- Performing ETL

- Learning about the data

- Hadoop, Alpine Miner, Open Refine, Data Wrangler

Model Planning

- Data exploration & variable selection

- Model selection

- Uses R, SQL Analysis Services

- SAS/ACCESS

Model Building

- Develop data set for training, testing, and production purposes

- Ensure that the training and test datasets are sufficiently robust for the model and analytical techniques

Communicate Results

- Articulate!

- Determine if the results are statistically significant and valid

- Validate the IH

Operationalization

- Pilot testing

- Monitoring; retraining the model

Discovery

Data Preparation

In what phase(s) does iteration typically happens?

Model Building (1)

Iteration should stop when you reach this phase (1)